Machine for audience propensity ranking using internet of things (IoT) inputs

A system useful for generating consumer propensity information, the system comprising:

• a. a plurality of consumer machines connected to a network, each of the plurality of consumer machines configured to derive consumer data points associated with a plurality of consumers;
• b. a predictive segmentation machine in communication with the plurality of consumer machines over the network, the predictive segmentation machine comprising:
• i. a data layer executing instructions that cause the machine to:
• A. receive a plurality of input messages comprising at least a portion of the consumer data points derived by the plurality of consumer machines;
• B. read from the plurality of input messages the consumer input data points;
• and
• C. append to each of the consumer input data points one of a number of predictive segmentation attributes thereby creating a plurality of different combinations of consumer-attribute pairs;
• ii. a platform layer executing instructions that cause the machine to:
• A. generate a summary matrix, wherein generating the summary matrix comprises counting an amount of each consumer-attribute pair combination;
• B. convert each of the counts of consumer-attribute pair combinations to a statistical metric relaying propensity information;
• C. construct an insight graph displaying the propensity information;
• and
• iii. a client layer configured executing instructions that cause the machine to perform at least one of the following result relaying tasks:
• A. transmit the insight graph to a user interface at a client machine for displaying the insight graph at a client location;
• B. append the propensity information to data housed at a client database;
• and
• C. match the propensity information to a matching universe to generate an audience report, wherein the matching universe comprises audience data pertaining to consumers associated with the plurality of devices.

A method useful for generating propensity information, the method comprising the steps of:

• a. receiving at a predictive segmentation machine a plurality of input messages from a plurality of consumer devices in communication with the predictive segmentation machine across a network, each of the plurality of consumer devices configured to derive consumer data associated with a number of consumers, and the plurality of input messages comprising at least a portion of the consumer data points derived by the plurality of consumer devices;
• b. reading from the plurality of input messages the consumer data points received from the consumer devices;
• c. appending to each of the consumer input data points one of a number of predictive segmentation attributes thereby creating a plurality of consumer-attribute pair combinations;
• d. counting a total number of consumer-attribute pair combinations;
• e. converting the total number of consumer-attribute pair combinations into a statistical metric, wherein each statistical metric is an index value relaying propensity information;
• f. and constructing an insight graph displaying the propensity information.